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Students' Misconceptions about Semiconductors and Use of Knowledge in Simulations

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Title: Students' Misconceptions about Semiconductors and Use of Knowledge in Simulations
Language: English
Authors: Nelson, Katherine G., McKenna, Ann F., Brem, Sarah K., Hilpert, Jonathan, Husman, Jenefer, Pettinato, Eva
Source: Journal of Engineering Education. Apr 2017 106(2):218-244.
Availability: Wiley Periodicals, Inc. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA
Peer Reviewed: Y
Page Count: 27
Publication Date: 2017
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: College Students, Engineering Education, Misconceptions, Knowledge Level, Electronic Equipment, Simulation, Prior Learning, Scientific Concepts
DOI: 10.1002/jee.20163
ISSN: 1069-4730
Abstract: Background: Little research exists on students' misconceptions about semiconductors, why they form, and what role educational resources like simulations play in misconception formation. Research on misconceptions can help enhance student learning about semiconductors. Purpose (Hypothesis): This project sought to identify students' misconceptions about three semiconductor phenomena -- diffusion, drift, and excitation -- and to determine if prior knowledge, knowledge acquired from watching animated simulations, or both were related to students' misconceptions. We hypothesized that students would hold misconceptions about those phenomena and that students' prior knowledge and knowledge acquired from watching animated simulations would be associated with their misconceptions. Design/Method: Forty-one engineering students completed an instrument that asked questions about three semiconductor phenomena after the students had observed the animated simulations. Responses were analyzed and coded using two frameworks: misconception and knowledge use. Results: Misconceptions were prevalent for all three phenomena. Misconceptions were associated with use of incorrect prior knowledge, a combination of correct or incorrect prior knowledge, and the knowledge acquired from watching the animated simulations alone or in combination with correct and incorrect prior knowledge. Misconceptions indicated a lack of understanding of chemistry and physics concepts. Conclusions: Findings indicate that students hold many misconceptions about semiconductor phenomena. These misconceptions were common among our participants. The knowledge acquired from the animated simulations alone or in combination with prior knowledge could reinforce or contribute to misconception formation. Our findings can guide instructors to use or create better simulations to aid student learning.
Abstractor: As Provided
Entry Date: 2020
Accession Number: EJ1254474
Database: ERIC
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  Value: <anid>AN0122635334;6m401apr.17;2018Jun29.11:57;v2.2.500</anid> <title id="AN0122635334-1">Students' Misconceptions about Semiconductors and Use of Knowledge in Simulations. </title> <p>Background: Little research exists on students’ misconceptions about semiconductors, why they form, and what role educational resources like simulations play in misconception formation. Research on misconceptions can help enhance student learning about semiconductors. Purpose (Hypothesis): This project sought to identify students’ misconceptions about three semiconductor phenomena – diffusion, drift, and excitation – and to determine if prior knowledge, knowledge acquired from watching animated simulations, or both were related to students’ misconceptions. We hypothesized that students would hold misconceptions about those phenomena and that students’ prior knowledge and knowledge acquired from watching animated simulations would be associated with their misconceptions. Design/Method: Forty‐one engineering students completed an instrument that asked questions about three semiconductor phenomena after the students had observed the animated simulations. Responses were analyzed and coded using two frameworks: misconception and knowledge use. Results: Misconceptions were prevalent for all three phenomena. Misconceptions were associated with use of incorrect prior knowledge, a combination of correct or incorrect prior knowledge, and the knowledge acquired from watching the animated simulations alone or in combination with correct and incorrect prior knowledge. Misconceptions indicated a lack of understanding of chemistry and physics concepts. Conclusions: Findings indicate that students hold many misconceptions about semiconductor phenomena. These misconceptions were common among our participants. The knowledge acquired from the animated simulations alone or in combination with prior knowledge could reinforce or contribute to misconception formation. Our findings can guide instructors to use or create better simulations to aid student learning.</p> <p>misconceptions; semiconductors; prior knowledge; simulations</p> <p>Semiconductors are an essential material in modern electronics and are designed and produced by physicists and electrical and materials engineers. Semiconductor science has been taught in undergraduate engineering programs dating back to Shockley's textbook Electrons and Holes in Semiconductors (1950), so the concepts covered and the level of depth have been well established. Semiconductors have many unique properties that make them difficult to understand, and educators may not be aware of these difficulties when they teach their students about semiconductors (Nelson, Husman, Brem, Bowden, & Honsberg, [<reflink idref="bib28" id="ref1">28</reflink>] ). Although education researchers have identified many general difficulties students face when learning, the study of misconceptions has become especially relevant for scientific phenomena (Carey, [<reflink idref="bib4" id="ref2">4</reflink>] ), and for semiconductors in particular (Nelson et al., [<reflink idref="bib28" id="ref3">28</reflink>] ).</p> <p>Students develop misconceptions from their interactions with the physical world, through instruction, and as they reconcile these interactions and learning experiences with their prior knowledge (Clement, [<reflink idref="bib9" id="ref4">9</reflink>] , [<reflink idref="bib10" id="ref5">10</reflink>] ; McDermott & Shaffer, [<reflink idref="bib27" id="ref6">27</reflink>] ; Nicoll, [<reflink idref="bib31" id="ref7">31</reflink>] ; Picciarelli, di Gennaro, Stella, & Conte, [<reflink idref="bib33" id="ref8">33</reflink>] ; Steinberg, Brown, & Clement, [<reflink idref="bib41" id="ref9">41</reflink>] ; Streveler, Olds, Miller, & Nelson, [<reflink idref="bib43" id="ref10">43</reflink>] ). Misconception research has considered the types and prevalence of misconceptions students have related to semiconductor science (Chen, Pam, Sung, & Chang, [<reflink idref="bib6" id="ref11">6</reflink>] ; Fayyaz, Iqbal, & Hashmi, [<reflink idref="bib17" id="ref12">17</reflink>] ; García‐Carmona, & Criado, [<reflink idref="bib18" id="ref13">18</reflink>] ; Wettergren, [<reflink idref="bib47" id="ref14">47</reflink>] ), but researchers have not used dynamic animated simulations to identify misconceptions about semiconductors. Nor have previous studies considered why misconceptions about semiconductors form. The purpose of our study was to demonstrate the presence and prevalence of students’ misconceptions about three semiconductor phenomena through the use of dynamic animated simulations, and to identify reasons why students formed misconceptions.</p> <hd id="AN0122635334-2">Literature Review</hd> <p>Misconceptions can be detrimental to learning (Clement, [<reflink idref="bib9" id="ref15">9</reflink>] , [<reflink idref="bib10" id="ref16">10</reflink>] ; McDermott & Shaffer, [<reflink idref="bib27" id="ref17">27</reflink>] ; Nicoll, [<reflink idref="bib31" id="ref18">31</reflink>] ; Streveler et al., [<reflink idref="bib43" id="ref19">43</reflink>] ) because they are resistant to change; they act as learning barriers and cognitive bottlenecks that must be overcome for additional learning to occur (Dole & Sinatra, [<reflink idref="bib14" id="ref20">14</reflink>] ). When students believe they understand a phenomenon or concept, their novice prior knowledge of the physical world reinforces their sensory perceptions of the concept (Sinatra, Brem, & Evans, [<reflink idref="bib16" id="ref21">16</reflink>] ), so they do not realize that they have formed a misconception (Evans, [<reflink idref="bib16" id="ref22">16</reflink>] ). In order to overcome the misconception, students must engage in conceptual change, but they often fail (Dole & Sinatra, [<reflink idref="bib14" id="ref23">14</reflink>] ).</p> <p>Cognitive psychologists have identified numerous misconceptions students have about the physical world. For example, Clement ([<reflink idref="bib9" id="ref24">9</reflink>] ) showed that novice physics students have misconceptions about Newtonian motion and forces. In engineering, specifically in thermal sciences, students have a hard time understanding the differences between steady state and equilibrium situations and the differences between energy and heat (Streveler et al., [<reflink idref="bib43" id="ref25">43</reflink>] ). For circuits, students incorrectly describe current as being like the flow of water (Picciarelli et al., [<reflink idref="bib33" id="ref26">33</reflink>] ), and students use the terms in Ohm's law interchangeably (McDermott & Shaffer, [<reflink idref="bib27" id="ref27">27</reflink>] ).</p> <p>Previous studies in engineering education have examined the misconceptions students have about the phenomena that underlie semiconductor processes. Using interviews, Wettergren ([<reflink idref="bib47" id="ref28">47</reflink>] ) examined undergraduate engineering students’ conceptions of diffusion, holes, and doping in semiconductors and found that students held incomplete or incorrect conceptions of these scientific phenomena. Misconceptions about diffusion consist of statements that the electrons pass through a barrier, dispersing evenly toward areas of greater concentration, or that they move in and out of the material. Using concept maps and structured interviews, Fayyaz et al. ([<reflink idref="bib17" id="ref29">17</reflink>] ) found that students struggled with semiconductor concepts related to holes, doping, drift and diffusion current, and temperature effects. In the case of drift and diffusion current, the participants confused conventional current with diffusion and drift current (Fayyaz et al., [<reflink idref="bib17" id="ref30">17</reflink>] ). Garcia‐Carmona and Criado ([<reflink idref="bib18" id="ref31">18</reflink>] ) examined the interaction of electron‐hole pairs and doping using a content assessment and personal interviews. They found that students aged 14 to 15 thought that the hole was evidence of damage in the crystalline structure of the semiconductor material and that doping was a means to repair the material's defect (Garcia‐Carmona & Criado, [<reflink idref="bib18" id="ref32">18</reflink>] ). Using a diagnostic test, Chen et al. ([<reflink idref="bib6" id="ref33">6</reflink>] ) found that undergraduate engineering students had misconceptions about holes, drift, diffusion, diodes, and basic circuits. Despite the work done on misconceptions in engineering education, the topic is still under researched, and some experts have encouraged the engineering education community to remedy this problem (e.g., Streveler, Litzinger, Miller, & Steif, [<reflink idref="bib42" id="ref34">42</reflink>] ), especially for misconceptions about semiconductors (Nelson et al., [<reflink idref="bib28" id="ref35">28</reflink>] ).</p> <p>What about the phenomena that underlie semiconductor processes is especially troublesome to students? In semiconductors, dynamic processes occur at an atomic level, a level that is not observable by the human eye. Because students cannot observe these phenomena, they struggle to grasp what occurs. To combat the inherent difficulties of learning dynamic, unobservable phenomena, some researchers have suggested the use of simulations and other educational technologies to promote students’ understanding (Nelson, Koselar, & Husman, [<reflink idref="bib28" id="ref36">28</reflink>] ; Chen et al., [<reflink idref="bib6" id="ref37">6</reflink>] ). Simulations are used in a wide variety of teaching environments to promote learning because they efficiently convey phenomena (Deliktas, [<reflink idref="bib12" id="ref38">12</reflink>] ) and may promote learning (Lowe, [<reflink idref="bib25" id="ref39">25</reflink>] ; Ainsworth, [<reflink idref="bib1" id="ref40">1</reflink>] ). In engineering education, simulations have been suggested as a means to enhance instruction of electrical engineering concepts (e.g., Jimenez‐Leube, Alemendra, Gonzalez, & Sanz‐Maudes, [<reflink idref="bib22" id="ref41">22</reflink>] ), including those about semiconductor phenomena (e.g., Nelson et al., [<reflink idref="bib28" id="ref42">28</reflink>] ). Building from that suggestion, researchers have promoted simulations to enhance understanding of phenomena that are too small to see (Kelly & Jones, [<reflink idref="bib24" id="ref43">24</reflink>] ; Sanger, Brecheisen, & Hynek, [<reflink idref="bib35" id="ref44">35</reflink>] ) and are dynamic (Chi, Roscoe, Slotta, Roy, & Chase, [<reflink idref="bib8" id="ref45">8</reflink>] ). Lastly, simulations of dynamic phenomena can be used to determine what concepts students struggle with – that is, to assess students’ misconceptions (Brem, Stump, Sinatra, Reichenberg, & Heddy, [<reflink idref="bib3" id="ref46">3</reflink>] ).</p> <hd id="AN0122635334-3">Research Design</hd> <p>Our study was guided by Ericsson and Simon's ([<reflink idref="bib15" id="ref47">15</reflink>] ) method of verbalization of thought. Researchers using this model can identify individual's cognitive processes as the individual verbalizes what they are thinking through a think‐aloud or written‐approach. The method assumes that human cognition is an information processing system and the information a researcher gathers from the subject's verbalizations (written or verbal reports or responses) represents their conceptions as the engage in a task, such as watching an animated simulation. Ericsson and Simon ([<reflink idref="bib15" id="ref48">15</reflink>] ) argued that participant conceptions are best identified while they verbalize their thoughts because participants have to focus their attention on the information being processed in their short‐term memory during verbalization. As a result, less information is processed by the participants during verbalization, and thus concepts are less likely to be altered. When researchers use interviews or concept mapping, participants’ concepts are more likely to be altered because they have more time to process the information.</p> <p>Different approaches have been utilized to understand verbalization data. Building on Ericsson and Simon ([<reflink idref="bib15" id="ref49">15</reflink>] ), Chi ([<reflink idref="bib7" id="ref50">7</reflink>] ) described the verbal analysis approach, a methodology that utilizes an individual's knowledge representation, or conceptual representation. Unlike Ericsson and Simon ([<reflink idref="bib15" id="ref51">15</reflink>] ) though, Chi's ([<reflink idref="bib7" id="ref52">7</reflink>] ) verbal analysis approach focuses on what an individual knows about a concept instead of how the concept is applied, as with problem solving. Chi's ([<reflink idref="bib7" id="ref53">7</reflink>] ) approach is ideal for misconception research because it can directly identify an individual's conception – their knowledge representation.</p> <hd id="AN0122635334-4">Frameworks</hd> <p>We sought to identify students’ misconceptions about three semiconductor phenomena – diffusion, drift, and excitation – and why those conceptions arose. Two conceptual frameworks were used. The first was the misconception framework. We defined misconceptions as inaccurate verbal descriptions of the scientific phenomena (Smith, Disessa, & Rochelle, 1993; Vosniadou, [<reflink idref="bib45" id="ref54">45</reflink>] ). The second was the knowledge use framework. Knowledge use represents where the concept came from, or was formed from, for example, from prior knowledge or newly acquired knowledge through observation.</p> <hd id="AN0122635334-5">Misconception framework</hd> <p>According to the cognitive constructivist perspective, persons construct knowledge using prior knowledge, and newly acquired knowledge from their sensory perceptions or interactions with the world around them (Cobb, [<reflink idref="bib11" id="ref55">11</reflink>] ). Knowledge is a memory representation, or a multidimensional packet of organized information in the mind (Jetton, Rupley, & Wilson, [<reflink idref="bib21" id="ref56">21</reflink>] ). As a student acquires knowledge, these representations are enriched and restructured (Piaget, [<reflink idref="bib32" id="ref57">32</reflink>] ). Misconceptions occur when an incorrect knowledge representation has formed about a specific concept (Smith et al., 1993). Misconceptions can be defined as a person's incorrect representation of a concept.</p> <hd id="AN0122635334-6">Knowledge use framework</hd> <p>Students develop misconceptions because of their sensory perceptions of the world and sometimes through instruction (Clement, [<reflink idref="bib9" id="ref58">9</reflink>] ; Kaiser, McCloskey, & Proffitt, [<reflink idref="bib23" id="ref59">23</reflink>] ). In instruction, for example, educators typically use imperfect models to teach difficult concepts. Nicoll ([<reflink idref="bib31" id="ref60">31</reflink>] ) demonstrated that when educators try to simplify learning about the atom by likening it to the solar system, students think that electrons are solid bodies that move around an atom like planets orbit the sun; this imperfect model offers little explanation for the role of electrons in atomic bonding (Nicoll, [<reflink idref="bib31" id="ref61">31</reflink>] ). Building from that imperfect model, as students blend this prior knowledge with new knowledge acquired from their sensory perceptions of the world or through instruction, they can create intermediate misconceptions (Vosniadou & Brewer, [<reflink idref="bib46" id="ref62">46</reflink>] ). Kaiser, McCloskey, and Proffitt ([<reflink idref="bib23" id="ref63">23</reflink>] ) found that students develop misconceptions about force and motion with a U‐shape developmental pattern. The younger students and older students understand the concepts. In contrast, the middle students have misconceptions; their previous prior knowledge of the phenomenon is supported by their sensory perceptions of the world. However, as the middle students take in new information during instruction about the phenomenon, they struggle to integrate what they are learning with what they already know and form a misconception. Considering this, for this study, we assumed that there are three main forms of knowledge used: prior knowledge, newly perceived and acquired knowledge, and the combination of the two.</p> <hd id="AN0122635334-7">Research Questions</hd> <p>Using the protocol analysis method (Ericsson & Simon, 1984) in conjunction with Chi's ([<reflink idref="bib7" id="ref64">7</reflink>] ) verbal analysis method, we had engineering students complete a instrument where they verbalized – through written reports – what they understood about animated simulations of three semiconductor phenomena. Participant responses were coded according to the two frameworks: misconception and knowledge use. We had three research questions:</p> <p>What misconceptions do students have about diffusion, drift, and excitation in semiconductors?</p> <p>What knowledge did students use when they had misconceptions about these semiconductor phenomena?</p> <p>Is the presence of misconceptions for these three semiconductor phenomena associated with the animated simulations used to demonstrate the phenomena, and if so, how?</p> <hd id="AN0122635334-8">Method</hd> <hd id="AN0122635334-9">Participants</hd> <p>Participants were recruited from three introductory circuits course sections taught in the electrical engineering department at a large university in the southwestern United States. An introductory circuits course was chosen because it was assumed enrolled students had a basic understanding of physics and had been previously exposed to concepts related to electricity and basic material properties. A total of 41 engineering undergraduates participated in the study, each receiving $30 in compensation. Participants consisted of 33 males and eight females and had taken at least one college‐level semester‐long physics course (one course, 10%; two courses, 76%; three courses, 10%; and four courses, 4%). The majority of participants had just completed their second college‐level semester‐long physics course on electricity and magnetism. A minority of participants had taken one or more college‐level semester‐long materials science courses (29%), and none had taken a course on semiconductors. Participants were predominantly majoring in mechanical engineering (32%), followed by biomedical (27%), aerospace (17%), electrical (12%), industrial (8%), and chemical and computer science (2% each). Participants primarily described themselves as second‐year undergraduates (freshman, 5%; sophomore, 80%; junior, 12%; and senior, 3%), and of those that reported it, the overwhelming majority were between the ages of 18 and 24 (18 to 24, 90%; 25 to 34, 3%; and 35 to 44, 6%).</p> <hd id="AN0122635334-10">Materials and Procedure</hd> <p>The study followed the verbalization of thought method and used an instrument that elicited verbal reports (Ericcson & Simon, [<reflink idref="bib15" id="ref65">15</reflink>] ) to determine misconceptions and knowledge use. In the instrument's protocol, participants were asked to provide written responses to questions that probed their understanding of simulations for three dynamic and atomic‐level phenomena – diffusion, drift, and excitation. Participants should have encountered all three phenomena prior to their enrollment in their introductory circuits course in physics, chemistry, or both, but not necessarily for semiconductor subject matter.</p> <hd id="AN0122635334-11">Phenomena simulations</hd> <p>Each phenomenon was simulated using animations developed in Adobe Flash Professional CS6. The use of animated simulations for the instrument's tasks has been used previously to identify students’ misconceptions about dynamic and unobservable phenomena (e.g., Brem et al., [<reflink idref="bib3" id="ref66">3</reflink>] ). Also, animated simulations were used because other methods described in the literature to identify misconceptions relied on representing the phenomena as static, or fixed and not moving. Animated simulations would better demonstrate the phenomena as dynamic and could therefore be more effective in identifying misconceptions.</p> <p>Each simulation demonstrated the phenomenon at the electron level. The electrons in the animated simulations were represented by icons that captured the appearance of the electron (i.e., the electron was depicted as a sphere). The participants were told before the simulation began what icons would be used and what they would represent. Further, participants were told what they would view in each simulation; they were aware that they would be observing a semiconductor and were directed toward specific actions. This focusing was done to ensure that the participants would attend to the relevant actions in the simulation. Each animated simulation was video‐captured using Camtasia 8.0 so that simulations could be shown to all participants. A key was used to identify all of the important elements in the simulation so that the participants could use it as a continuous reference as they viewed the simulations. No explanatory text or voice‐overs were included in order to reduce the cognitive load on the participants during simulation viewing (Sweller & Chandler, 1994). Each simulation was designed by experts in electrical and material science engineering as an instructional aide to demonstrate semiconductor phenomena. At the time of the study, the simulations were being utilized to teach students worldwide about semiconductors. No work has been done to assess their efficacy, even though they correctly represent each of the phenomena. Figures [NaN] , [NaN] , and [NaN] are screenshots of the animated simulation for each phenomenon.</p> <p>The three semiconductor phenomena we simulated were diffusion, drift, and excitation. Diffusion is an electron transport phenomenon whereby electrons move from areas of high concentration to low concentration as a result of the interactions of the electrons. Diffusion in semiconductors involves carriers, the electrons, and the holes that participate in conduction. The electron carriers appear to move in a net direction from areas of high concentration of electron carriers toward areas of lower concentration of electron carriers in the semiconductor (Honsberg & Bowden, [<reflink idref="bib20" id="ref67">20</reflink>] ). The movement is actually the result of the small but additive effects of the electron carriers interacting with each other, randomly colliding, and producing this pattern. The animated simulation for diffusion (Figure [NaN] ) demonstrated how the electrons (spheres) concentrated in one area would spread out over time.</p> <p>In the phenomenon of drift, electron carriers generally move in a certain net direction opposite to the applied electric field placed on the device. As with diffusion, electron carriers move in a certain net direction due to random motion. Unlike the electron carriers in diffusion, the net velocity of that electron carrier for drift is the resultant of two vectors (versus one for diffusion) that affect the net direction. The first vector is the random vector, and the second is the vector from the force of the electric field in the opposite direction of the electric field. Movements of electron carriers are the result of both the random additive effects of electron interactions and the electric field. Therefore, drift is similar to diffusion with one additional force acting on the system. The animated simulation for drift (Figure [NaN] ) demonstrated the random movement of the electrons with and without an electric field placed on the semiconductor. When the electric field was off, the electrons bounced around randomly. When the electric field was on, the electrons bounced around randomly, but the overall net direction of movement was opposite to the electric field.</p> <p>Excitation occurs when energy is transferred to a semiconductor device and an electron gains enough of that energy to break its bond and move into the conduction band. Excitation was depicted using the semiconductor field of photovoltaics – the design and construction of solar energy semiconductor devices. The energy being transferred to the device is in the form of a photon. The photon (if it had enough energy) excited the electron by freeing it from its bound atomic state. The electron jumped from its bound atomic energy state (valance band) to a higher energy state (conduction band) such that it moved freely about the semiconductor material lattice. Once the electron was in the conduction band, it became part of the semiconductor's current. In the simulation for excitation (Figure [NaN] ), the animated simulation demonstrated how different photons (squiggly lines with arrows) with different amounts of energy would hit the semiconductor and transfer energy to an electron (sphere) such that under certain circumstances a given electron may or may not be excited into the conduction band.</p> <hd id="AN0122635334-12">Protocol</hd> <p>The instrument's protocol consisted of separate viewings and associated questions about each phenomenon's animated simulation, followed by a set of demographic questions. The participants completed open‐ended and Likert‐style questions related to each animated simulation. The protocol and associated instrument was adapted from Brem et al. ([<reflink idref="bib3" id="ref68">3</reflink>] ) and had questions related to diffusion, drift, and excitation. Participants were asked to view the animated simulation of each phenomenon individually and then answer a series of questions related to what they saw (specific questions can be found in Appendix A). Each of the phenomena was situated in the context of a solar cell. The instrument protocol took approximately 90 minutes to complete.</p> <p>Each animated simulation lasted approximately 90 seconds. Participants viewed the entire simulation two times before proceeding to the questions. The instrument was initially pilot tested on experts and graduate students in both semiconductor engineering (expert and mid‐level knowledge of semiconductor concepts, novice‐level knowledge of the protocol or verbal analysis method) and cognitive science (novice‐level knowledge of semiconductor concepts, expert and mid‐level knowledge of protocol or verbal analysis method). Using the comments gathered during the pilot test, it was agreed that two 90‐second simulation viewings would offer a sufficient amount of time for the participants to see the key features of the phenomenon.</p> <p>There were five questions for the diffusion section, seven questions for the drift section, and six questions for the excitation section in the instrument. The number of questions varied because of the nature of each animated simulation. Each section had three main questions. Broad questions were used at first (e.g., questions 1 and 2 below), moving to more specific questions that asked about key aspects of the phenomena (e.g., question 3 below). For example, the following series of questions was used for diffusion:</p> <p>Describe the movement of the electron(s) in the solar cell? Use as much detail as possible.</p> <p>Based on your knowledge of physics and electrons, what determines how and where the electrons move in the solar cell? Use as much detail as possible.</p> <p>Imagine electrons in a similar solar cell under the same scenario moving again. How similar do you think the movement of the electrons would be to what you observed in the video?</p> <p>The questions were designed to be similar for each of the phenomena. All questions for the three modules can be found in Appendix A.</p> <p>Participants were instructed to write with as much detail as possible when responding to the questions. All questions required a response. Participants were not able to go back to change their responses. The entire instrument was pilot tested (as just described), and changes were made based on participants’ suggestions; better explanations of the simulations were provided to reduce participants’ confusion; some of the language was altered in the questions for clarity; and more demographic questions were added. The final version of the instrument contained 39 questions: one question to obtain consent to participate in the study, 28 questions related to the phenomena animated simulations, and 10 demographic questions.</p> <hd id="AN0122635334-13">Analysis</hd> <p>The intent of our study was to first identify participants’ conceptions about the three phenomena; therefore the participant's written responses to the questions were analyzed following Chi's ([<reflink idref="bib7" id="ref69">7</reflink>] ) approach. The written responses were used to ascertain participants’ conceptions about diffusion, drift, and excitation. Second, relationships were explored between identified misconceptions and knowledge use. Therefore, the analysis was a blend of qualitative and quantitative research methods.</p> <hd id="AN0122635334-14">Qualitative</hd> <p>The verbal analysis method requires both top‐down (deductive) and bottom‐up (inductive) approaches to developing codes (Chi, [<reflink idref="bib7" id="ref70">7</reflink>] ); we used themes to develop the codes. From a deductive perspective, the questions for each phenomenon's animated simulation were framed using existing theory on misconceptions (Vosniadou, [<reflink idref="bib45" id="ref71">45</reflink>] ) and their formation (Nicoll, [<reflink idref="bib31" id="ref72">31</reflink>] ; Vosniadou & Brewer, [<reflink idref="bib46" id="ref73">46</reflink>] ). As such, some of the developed codes represented misconceptions about semiconductors already documented in the literature. When approaching the written responses inductively, we were guided by grounded theory research methods, so that additional themes and hypotheses could emerge from the data beyond what has already been documented in the literature (Charmaz, [<reflink idref="bib5" id="ref74">5</reflink>] ). Overall, we sought to capture as much information from the written responses as possible (Chi, [<reflink idref="bib7" id="ref75">7</reflink>] ).</p> <p>The verbal analysis method has been used extensively for identification of misconceptions, especially related to identifying misconceptions for dynamic phenomena (Brem et al., [<reflink idref="bib3" id="ref76">3</reflink>] ). We opted for analysis of written responses instead of the typical think‐aloud, oral approach because research has shown no major difference in results regarding thinking processes between the two approaches (Ericsson & Simon, [<reflink idref="bib15" id="ref77">15</reflink>] ), and we wanted to collect data from a large number of participants without the typical time constraints associated with the think‐aloud method. Additionally, because a written response requires an extra level of cognitive encoding by the participant as they write (i.e., the participant must pay attention to what they are writing), the use of written responses by the participants may prevent mistakes they can make when they speak their thoughts in the think‐aloud approach.</p> <p>To capture the different features of the misconception and knowledge use frameworks, two codebooks were developed from the participants’ written responses: the misconception codebook and the knowledge use codebook. The first author conducted a semi‐open coding of the written responses to capture themes about misconceptions and knowledge use, which were subsequently used to construct the codebook. The first author is an expert in engineering and education.</p> <hd id="AN0122635334-15">Misconception codebook</hd> <p>Coding for misconceptions was guided by misconceptions about semiconductors reported in the literature. The first author first took a deductive approach and looked for the themes described by Chen et al. ([<reflink idref="bib6" id="ref78">6</reflink>] ), Fayyaz, Iqbal, and Hashmi ([<reflink idref="bib17" id="ref79">17</reflink>] ), and Wettergren ([<reflink idref="bib47" id="ref80">47</reflink>] ) that matched or were similar to themes that were observed in the participants’ responses. Themes from those three sources that differed from the themes observed in this study were not included. Once these comparisons were made, the first author went back through the data to allow for additional themes to emerge using an inductive approach. Through the process, it became clear that different themes were observed for diffusion, drift, and excitation because misconceptions were observed to be phenomenon‐specific. The themes were used to develop the misconception codebook. A total of 16, 18, and 21 misconception codes were developed for the participants’ responses for diffusion, drift, and excitation phenomenon, respectively. Some similar codes were developed to capture similar themes among the different participant responses for each phenomenon. Misconception codes were validated using an expert in semiconductor science and photovoltaics, and the codebook was validated using the third author, an expert in cognitive science who has conducted extensive research related to misconception identification using written responses from verbalization of thought protocols that utilized animated simulations.</p> <p>For each phenomenon, misconception themes and associated codes were grouped to represent a higher level theme. Each higher level theme was based on the links participants made between the different misconceptions. Six higher level misconception themes were developed. Similar to the validation of misconception codes, these higher level themes were validated using experts in both semiconductor science and engineering education and through correlational analysis of misconceptions within each theme, as described by Nelson ([<reflink idref="bib30" id="ref81">30</reflink>] ).</p> <hd id="AN0122635334-16">Knowledge use codebook</hd> <p>Coding for knowledge use was done inductively, guided by the themes of knowledge use that emerged from that data. The first author allowed themes to emerge from the data that were related to the type of knowledge that the participants used to describe the phenomena when responding to the questions. Unlike with the misconception themes, themes that emerged for knowledge use could be applied to all three phenomena. For example, we were not looking at specific types of prior knowledge (e.g., prior knowledge about diffusion), but instead we were looking at use of prior knowledge in general. The themes were used to develop the knowledge use codebook. Seven prior knowledge codes represented the themes from the data. Themes and the codebook were validated by the second author, an expert in engineering learning.</p> <hd id="AN0122635334-17">Coding</hd> <p>Participants’ responses were coded first for the misconceptions and second for knowledge use. During coding, the first author examined each participant's response for each question. If the response had any of the themes, it was coded for that theme's associated misconception or knowledge use. Grammar and other writing mistakes were ignored so that the coding captured the broader context of each written response. As described by Chi ([<reflink idref="bib7" id="ref82">7</reflink>] ), the broader context of the response was used when inaccurate vocabulary was observed. For example, if a participant specifically stated that electrons move in the direction of the electric field (misconception), but then provided further detail about what that movement was and ultimately described that electrons move opposite to the electric field, the participant's response would not be coded as a misconception. Alternative codes for the misconceptions included U (uncodable) or A (absent of misconception). Uncodable represented all responses that were either missing or unreadable. Absent of misconception responses included those that could not be coded as a misconception or uncodable, indicating that a misconception was not present in the response. Absent of misconception was used instead of correct conception because a lack of misconception does not necessarily mean a correct conception (e.g., the text could be off‐topic). A similar code was not utilized for the knowledge use codebook because we were concerned only with whether the prior knowledge was associated with a correct or misconception response.</p> <p>To assess interrater reliability, the codebook and 14 participant responses were given to the sixth author, who also has a background in engineering and education. The codebooks included a list of guidelines for the sixth author to follow when coding, in addition to descriptions of each phenomenon. During this time, the first author worked through examples with the sixth author in order to make sure the sixth author applied the codes correctly. The sixth author also worked independently on other examples, asking for help as needed until the sixth author felt prepared to code the responses. The sixth author had no contact with the first author during their actual coding of the responses. The first and sixth authors applied the codes with 0.85 agreement for misconceptions and 0.91 for knowledge use. Any disagreements were resolved through discussion.</p> <hd id="AN0122635334-18">Quantitative</hd> <p>The final codes were recoded into dichotomous variables. In the case of the misconception coding, each misconception was coded as a 1, and absent of misconceptions were coded as a 0. All themes captured from the written responses were coded as a 1. Uncodable was marked as NA and treated as missing data, and was excluded from the quantitative analysis. The data were coded using this presence‐absence dichotomous approach to determine the frequency of misconceptions and the frequency of knowledge use, and to discover any relationships between the two frequencies. Therefore, the quantitative data represented the number of times a particular misconception or information type was counted for each question for diffusion, drift, and excitation.</p> <p>Quantitative analyses were conducted using IBM SPSS statistical software to note any trends in the data and to run basic descriptive statistics. Composite scores were computed in SPSS to aggregate the counts of misconceptions for each phenomenon, and to aggregate the counts of knowledge use themes. Another composite score was generated in SPSS to create a higher level misconception theme for each phenomenon by counting the misconceptions observed for each theme for each phenomenon and dividing that by the total number of misconceptions. For example, if a participant held one of the three possible misconceptions that make up the higher level misconception theme M1 in 15 instances throughout their written responses for diffusion, the composite score for M1 was computed by dividing 15 by 3. Nonparametric statistical analyses were utilized because the data transformed from the coding process were count data. Relationships between misconceptions and knowledge use variables were assessed using Kendall's tau‐b correlation coefficients. All significant correlations were flagged and are reported. Kendall's tau‐b correlation was chosen because it is an accepted use of measuring association of count data (Gibbons, [<reflink idref="bib19" id="ref83">19</reflink>] ). Specifically, Kendall's tau‐b was chosen because the sample size was relatively small and because it is generally more conservative and accurate than Spearman's rho (another nonparametric measure of association). Kendall's tau‐b approaches asymptotic normality faster than Spearman's rho, for example, and therefore allows for more exact calculations of p‐values (Gibbons, [<reflink idref="bib19" id="ref84">19</reflink>] ). Calculation of exact p‐values for Kendall's tau‐b helps control for the possible accumulation of Type I errors more so than Spearman's rho, for example, when running multiple significance tests on the same sample (Arndt, Turvey, & Andreasen, [<reflink idref="bib2" id="ref85">2</reflink>] ).</p> <hd id="AN0122635334-19">Methodological Limitations</hd> <hd id="AN0122635334-20">Animated simulations</hd> <p>The simulations used in the study were constructed by experts in semiconductor science, but, as stated previously, the efficacy of the animated simulations as an educational tool has not been properly tested. These simulations were constructed to convey the concepts of diffusion, drift, and excitation. It is possible that the misconceptions observed could be associated with the animated simulations; however, the first and third authors evaluated these animated simulations during the protocol and instrument development as a tool to demonstrate the phenomena, not to prevent misconception formation.</p> <hd id="AN0122635334-21">Coding</hd> <p>The codebook and coding of the written responses were somewhat subjective. Different questions were used for each phenomenon, so it is reasonable to assume that different misconceptions and their associated frequencies should have emerged from the data. Different questions were used because there were differences between the phenomena. Inclusion of every question for each phenomenon would have made the length of the instrument a hindrance. Also, the codebook was developed differently for the misconceptions than for knowledge use. The misconceptions were mostly arrived at through deductive reasoning, whereas the knowledge use themes were mostly arrived at inductively. Approaches that use inductive reasoning can be limiting; they narrow the scope for the coder, so that misconceptions were possibly missed or that nuanced differences were not captured within certain misconceptions. Last, the responses were coded by one researcher. However, we believe that the guidance provided by experts in semiconductors, cognition, and engineering education helped lessen any possible problems during coding.</p> <hd id="AN0122635334-22">Statistical analyses</hd> <p>We utilized statistical analyses that have limitations because the data were nonparametric count data. Nonparametric statistics lack the power that parametric statistics have. That is, for comparing two groups with normal or similar distributions, parametric statistics are more effective.</p> <p>Nonparametric statistics, however, are useful at explaining the data that they represent, but do not provide the generalizability or extrapolation that parametric statistics allow. Last, since fewer statistical tests have been developed for nonparametric statistics, the types of questions that could be analyzed are limited.</p> <hd id="AN0122635334-23">Results</hd> <hd id="AN0122635334-24">Misconception Themes</hd> <p>Frequency of misconceptions was determined by counting the number of a misconception in a participant response for each question and averaging that number across all participants (for each phenomenon and aggregated for all three phenomena). The frequency of misconceptions was computed for diffusion (87%), drift (75%), and excitation (80%), and as an aggregate (80%). Similarly, the frequency of the uncodable and absent of misconception codes was computed for diffusion (4.3% and 8.9%), drift (2.0% and 23%), excitation (3.9% and 16.1%), and as an aggregate (3.3% and 16.7%), respectively.</p> <p>Each higher level misconception theme (M) and associated examples of a correct conception and a misconception are provided in Table [NaN] . Frequencies for each of these higher level misconception themes were computed by counting the number of times each misconception theme was observed for each participant response and dividing that by the total number of questions. For example, misconception theme M1 was observed in 82 responses for diffusion across all of the participants. The frequency was calculated by taking that count (<reflink idref="bib82" id="ref86">82</reflink>) and dividing it by the total number of questions for diffusion (five) multiplied by the number of participants (<reflink idref="bib41" id="ref87">41</reflink>), yielding a frequency of 0.40. Frequencies are listed in Table [NaN] .</p> <p>Misconception Themes</p> <p> <ephtml> <table><tr><th align="left">Theme</th><th align="center">Description</th><th align="center">Misconception example</th><th align="center">Correct conception example</th></tr><tr><td align="left">Ml</td><td align="left">Electrons move according to their attraction to a positive polarity or due to a potential difference.</td><td align="left">“When the electric field is off, the electrons move from negative to positive polarity.”</td><td align="left">“Electrons are constantly moving in every direction determined by their collisions with other electrons.”</td></tr><tr><td align="left">M2</td><td align="left">Individual electrons are predictable, not random, and/or possibly following rules that make it predictable or not random.</td><td align="left">“No its [sic] is not random because there are laws that govern the movement of electrons and these laws cannot be broken. This is not a random movement.”</td><td align="left">“Electron movement is random and unpredictable, even though there are rules that they follow.”</td></tr><tr><td align="left">M3</td><td align="left">Phenomenon is described as not predictable using justifications associated with M1.</td><td align="left">“No, not predictable. I know the in the [sic] material electrons move randomly because they s [sic] no attractions inside.”</td><td align="left">“Electron movement is not predictable because the path that each electron takes is unknown due to collisions.”</td></tr><tr><td align="left">M4</td><td align="left">Electron movement occurs because electrons move in an atom, even though they are still bonded.</td><td align="left">“An electron with a high enough energy will leave the valence band of an atom and orbit further from the positively charged nucleus.”</td><td align="left">“Electrons move about randomly when they have been freed from their atomic bond.”</td></tr><tr><td align="left">M5</td><td align="left">Describe other forces (e.g., gravity) for causing electron movement.</td><td align="left">“I believe that gravity has a huge effect on how and where the electron moves in the solar cell. Also, electric fields and energy play a major role as well.”</td><td align="left">“The forces causing electrons to move are caused by their collisions with other electrons.”</td></tr><tr><td align="left">M6</td><td align="left">Describe electron movement as a physical movement between energy states, or to conserve energy with physical movement.</td><td align="left">“Electrons are always moving randomly, so there could be a chance that the electron jumps to another band without the photon energy.”</td><td align="left">“When an electrons gains enough energy, it becomes excited into the conduction band.”</td></tr></table> </ephtml> </p> <p>1 Note: Examples provided are from the participants’ responses to the instrument.</p> <p>The most prevalent misconception themes for diffusion were theme M1 (40%) and theme M2 (34%). Those for drift were theme M1 (45%) and theme M2 (35%), and those for excitation were theme M1 (21%) and theme M4 (38%). For all three phenomena, themes M1, M2, and M3 were found; their presence indicates that participants held similar misconceptions for all three phenomena. For theme M1, participants had misunderstandings of electron movement related to theories of electricity and magnetism. For theme M2, participants described electron movement as predictable and not random, and sometimes noted that electrons follow rules determined by physics. For theme M3, participants explained the nonpredictability of electron movement (correct conception) using faulty understandings of electricity and magnetism (misconception). The animated simulations did not visually represent the phenomena in a way that would have made participants aware of the misconceptions for the higher level themes M1, M3, M4, M5, and M6. However, the predictable and random motions of electrons are shown in the animated simulations, visually dispelling the higher level theme M2 – the higher level theme that captures participants’ misconception that electrons move in deterministic and predictable paths.</p> <hd id="AN0122635334-25">Knowledge Use Themes</hd> <p>Seven knowledge use themes emerged from the participant's written responses. These themes are described and an example is given in Table [NaN] . Frequencies are displayed based on each of the knowledge use variables for each phenomenon in Table [NaN] . The descriptive statistics are not provided for knowledge use for each phenomenon (e.g., the frequency of knowledge use themes for diffusion would be 100%), because each participant response was coded for one information type or another.</p> <p>Knowledge Use Themes</p> <p> <ephtml> <table><tr><th align="left">Theme</th><th align="center">Description</th><th align="center">Example</th></tr><tr><td align="left">I1</td><td align="left">Response includes correct prior knowledge of the phenomenon.</td><td align="left">“The electrons are drawn to the positive side of the electric field. There is an equation that relates an electric field to the force and charge. Electrons have a tendency to be drawn more to positive side in nature.”</td></tr><tr><td align="left">I2</td><td align="left">Response includes incorrect prior knowledge.</td><td align="left">“Electrons move with attractions, electrons of the phenomenon are attracted to protons.”</td></tr><tr><td align="left">I3</td><td align="left">Response includes both incorrect and correct prior knowledge of the phenomenon.</td><td align="left">“While the electric field is on the electrons are being pulled to a certain direction. Yes, they may stray from this direction slightly with the repelling from each others [sic] negative attractions.”</td></tr><tr><td align="left">I4</td><td align="left">Response includes likely nonrelated prior knowledge of the phenomenon.</td><td align="left">“…unless there is a force acting on the electrons.”</td></tr><tr><td align="left">I5</td><td align="left">Response includes only the information provided from the animated simulation about the phenomenon (only describes what occurs in the animated simulation).</td><td align="left">“At the start, the density of electrons is highest within the center of the cell and lowest at the edges. Therefore, the electrons move from the area of highest concentration to the area of lowest concentration.”</td></tr><tr><td align="left">I6</td><td align="left">Response includes knowledge acquired from the animated simulation in addition to some form of prior knowledge (correct or incorrect) about the phenomenon.</td><td align="left">“The electrons will move in accordance with the electric field lines, which most of the time are pointing in the positive direction.”</td></tr><tr><td align="left">I7</td><td align="left">No prior knowledge included in the response, no knowledge acquired from the animated simulation.</td><td align="left">“I do not know anything about photons.”</td></tr></table> </ephtml> </p> <p>Frequencies of Misconception and Knowledge Use for Each Phenomenon</p> <p> <ephtml> <table><tr><th align="left">Theme</th><th align="center">Diffusion</th><th align="center">Drift</th><th align="center">Excitation</th></tr><tr><td align="left">Misconception</td></tr><tr><td align="left">M1</td><td align="char" char=".">0.40</td><td align="char" char=".">0.45</td><td align="char" char=".">0.21</td></tr><tr><td align="left">M2</td><td align="char" char=".">0.34</td><td align="char" char=".">0.35</td><td align="char" char=".">0.13</td></tr><tr><td align="left">M3</td><td align="char" char=".">0.11</td><td align="char" char=".">0.09</td><td align="char" char=".">0.25</td></tr><tr><td align="left">M4</td><td align="char" char=".">0.04</td><td align="char" char=".">0.00</td><td align="char" char=".">0.38</td></tr><tr><td align="left">M5</td><td align="char" char=".">0.04</td><td align="char" char=".">0.00</td><td align="char" char=".">0.00</td></tr><tr><td align="left">M6</td><td align="char" char=".">0.00</td><td align="char" char=".">0.00</td><td align="char" char=".">0.06</td></tr><tr><td align="left">Information type</td></tr><tr><td align="left">I1</td><td align="char" char=".">0.14</td><td align="char" char=".">0.33</td><td align="char" char=".">0.32</td></tr><tr><td align="left">I2</td><td align="char" char=".">0.44</td><td align="char" char=".">0.21</td><td align="char" char=".">0.29</td></tr><tr><td align="left">I3</td><td align="char" char=".">0.13</td><td align="char" char=".">0.21</td><td align="char" char=".">0.18</td></tr><tr><td align="left">I4</td><td align="char" char=".">0.08</td><td align="char" char=".">0.05</td><td align="char" char=".">0.07</td></tr><tr><td align="left">I5</td><td align="char" char=".">0.04</td><td align="char" char=".">0.04</td><td align="char" char=".">0.06</td></tr><tr><td align="left">I6</td><td align="char" char=".">0.09</td><td align="char" char=".">0.08</td><td align="char" char=".">0.03</td></tr><tr><td align="left">17</td><td align="char" char=".">0.08</td><td align="char" char=".">0.08</td><td align="char" char=".">0.05</td></tr><tr><td align="left">15 + 16</td><td align="char" char=".">0.13</td><td align="char" char=".">0.12</td><td align="char" char=".">0.09</td></tr></table> </ephtml> </p> <p>Frequencies for each knowledge use theme were computed by counting the number of times each theme was observed for each participant response and dividing that by the total number of questions. Theme I2 (44%), theme I1 (14%), and the combination of themes I5 and I6 (13%) were the most prevalent for diffusion. Theme I1 (33%), theme I2 (21%), and theme I3 (21%) were the most prevalent for drift. Theme I1 (32%) and theme I2 (30%) were the most prevalent for excitation. Themes I5 and I6 were combined because they both indicated the participants utilized the knowledge they gathered by watching the animated simulations. The majority of participant responses indicated they utilized correct prior knowledge of the phenomena, incorrect prior knowledge for the phenomena, or utilized prior knowledge (correct, incorrect, or both) in conjunction with the knowledge they acquired by watching the simulations.</p> <hd id="AN0122635334-26">Relationships between Themes</hd> <p>For each phenomenon, relationships were found between the misconception themes and knowledge use themes, as well as between the aggregated misconception scores. Kendall tau‐b correlation coefficients were calculated to assess these relationships. Exact probabilities are provided for the Kendall's tau‐b correlation test in Appendix B and C to further demonstrate significance. The following designation for strength of association was utilized: weak (<0.3), moderate (0.3 ≤ x < 0.7), and strong (>0.7). As seen in Table [NaN] , the knowledge use theme I1 was significantly and generally moderately negatively correlated with the aggregated misconception scores for diffusion (–.27, p < .05), drift (–.48, p < .05), and excitation (–.38, p < .05). These results for I1 indicate that when students used correct prior knowledge, they were less likely to have misconceptions. I2 was significantly and moderately positively correlated with the aggregated misconception scores for drift (.37, p < .05) and excitation (.38, p < .05). These results for I2 show that use of incorrect prior knowledge resulted in more misconceptions. Theme I1 was generally moderately negatively related to misconception themes for each phenomenon, whereas knowledge use theme I2 and theme I3 were generally moderately positively correlated with misconception themes for each phenomenon. Last, for diffusion, a significant and weakly negative relationship was observed between the theme M1 and theme I5 (–.28, p < .05), showing that misconceptions for diffusion were related to the knowledge participants acquired from watching the animated simulation (see Table [NaN] ).</p> <p>Prior Knowledge and Knowledge Use Composite Definitions</p> <p> <ephtml> <table><tr><th align="left">Composite theme name</th><th align="center">Information type themes included</th><th align="center">Definition</th></tr><tr><td align="left">I1 + 6</td><td align="left">Theme I1 and theme I6</td><td align="left">Responses include the knowledge acquired from watching the animated simulation and prior knowledge indicative of a correct knowledge representation of the phenomenon.</td></tr><tr><td align="left">I2 + 6</td><td align="left">Theme I2 and theme I6</td><td align="left">Responses include knowledge acquired from watching the animated simulation and prior knowledge indicative of an incorrect knowledge representation of the phenomenon.</td></tr><tr><td align="left">I3 + 6</td><td align="left">Theme I3 and theme I6</td><td align="left">Responses include knowledge acquired from watching the animated simulation and prior knowledge indicative of both a correct and incorrect knowledge representation of the phenomenon.</td></tr></table> </ephtml> </p> <hd id="AN0122635334-27">Simulations and Prior Knowledge</hd> <p>Additional analyses were conducted to determine the type of prior knowledge used because the relationships observed between knowledge use theme I6 (theme where responses indicated use of both prior knowledge and the knowledge acquired from watching the animated simulations) and the higher level misconception themes were unclear. Composite scores were generated using the theme I1 and theme I6 variables, theme I2 and theme I6, and theme I3 and theme I6 variables; then correlations were run with the misconception themes (at the theme level and at the phenomenon level) using the misconception aggregate scores for each phenomenon (see Table [NaN] ). Therefore, we could identify what types of prior knowledge (correct theme I1; incorrect, theme I2; or a combination, theme I3) were related to the different misconception themes. Composite definitions are given in Table [NaN] .</p> <p>Kendall's tau‐b Correlations for Misconception and Knowledge Use Themes</p> <p> <ephtml> <table><tr><th align="left">Theme type</th><th align="center">Misconception theme</th><th align="center" /></tr><tr><th align="center">Diffusion</th><th align="center">Drift</th><th align="center">Excitation</th><th align="center">Composite</th></tr><tr><th align="left">1</th><th align="center">2</th><th align="center">3</th><th align="center">4</th><th align="center">5</th><th align="center">1</th><th align="center">2</th><th align="center">3</th><th align="center">1</th><th align="center">2</th><th align="center">3</th><th align="center">4</th><th align="center">6</th><th align="center">Diffusion</th><th align="center">Drift</th><th align="center">Excitation</th></tr><tr><td align="left">I1</td><td align="char" char=".">−.24</td><td align="char" char=".">−.21</td><td align="char" char=".">.01</td><td align="char" char=".">−.11</td><td align="char" char=".">−.11</td><td align="char" char=".">−.42</td><td align="char" char=".">−.36</td><td align="char" char=".">−.18</td><td align="char" char=".">−.42</td><td align="char" char=".">.00</td><td align="char" char=".">−.16</td><td align="char" char=".">−.10</td><td align="char" char=".">−.21</td><td align="char" char=".">−.27</td><td align="char" char=".">−.48</td><td align="char" char=".">−.38</td></tr><tr><td align="left">I2</td><td align="char" char=".">.18</td><td align="char" char=".">.07</td><td align="char" char=".">−.01</td><td align="char" char=".">.29</td><td align="char" char=".">.08</td><td align="char" char=".">.29</td><td align="char" char=".">.21</td><td align="char" char=".">0.23</td><td align="char" char=".">.38</td><td align="char" char=".">−.05</td><td align="char" char=".">.26</td><td align="char" char=".">−.11</td><td align="char" char=".">.29</td><td align="char" char=".">.22</td><td align="char" char=".">.37</td><td align="char" char=".">.38</td></tr><tr><td align="left">I3</td><td align="char" char=".">.24</td><td align="char" char=".">−.15</td><td align="char" char=".">.18</td><td align="char" char=".">.01</td><td align="char" char=".">−.11</td><td align="char" char=".">.40</td><td align="char" char=".">.19</td><td align="char" char=".">.06</td><td align="char" char=".">.13</td><td align="char" char=".">−.15</td><td align="char" char=".">.05</td><td align="char" char=".">.37</td><td align="char" char=".">.16</td><td align="char" char=".">.19</td><td align="char" char=".">.33</td><td align="char" char=".">.23</td></tr><tr><td align="left">I4</td><td align="char" char=".">−.13</td><td align="char" char=".">.09</td><td align="char" char=".">.12</td><td align="char" char=".">−.02</td><td align="char" char=".">.17</td><td align="char" char=".">−0.08</td><td align="char" char=".">−.09</td><td align="char" char=".">−.16</td><td align="char" char=".">.04</td><td align="char" char=".">.08</td><td align="char" char=".">−.01</td><td align="char" char=".">−.20</td><td align="char" char=".">−.21</td><td align="char" char=".">.05</td><td align="char" char=".">−.18</td><td align="char" char=".">−.13</td></tr><tr><td align="left">I5</td><td align="char" char=".">−.28</td><td align="char" char=".">.20</td><td align="char" char=".">−.23</td><td align="char" char=".">.12</td><td align="char" char=".">−.13</td><td align="char" char=".">−.11</td><td align="char" char=".">.25</td><td align="char" char=".">−.12</td><td align="char" char=".">−.07</td><td align="char" char=".">−.02</td><td align="char" char=".">−.18</td><td align="char" char=".">−.15</td><td align="char" char=".">−.04</td><td align="char" char=".">−.06</td><td align="char" char=".">.03</td><td align="char" char=".">−.12</td></tr><tr><td align="left">I6</td><td align="char" char=".">.07</td><td align="char" char=".">.15</td><td align="char" char=".">.17</td><td align="char" char=".">.13</td><td align="char" char=".">−.18</td><td align="char" char=".">.02</td><td align="char" char=".">−.06</td><td align="char" char=".">.19</td><td align="char" char=".">−.05</td><td align="char" char=".">−.30</td><td align="char" char=".">.04</td><td align="char" char=".">.17</td><td align="char" char=".">.08</td><td align="char" char=".">.22</td><td align="char" char=".">.06</td><td align="char" char=".">.06</td></tr><tr><td align="left">I7</td><td align="char" char=".">−.23</td><td align="char" char=".">.10</td><td align="char" char=".">−.11</td><td align="char" char=".">−.18</td><td align="char" char=".">−.01</td><td align="char" char=".">.08</td><td align="char" char=".">.27</td><td align="char" char=".">−.10</td><td align="char" char=".">−.29</td><td align="char" char=".">.13</td><td align="char" char=".">−.21</td><td align="char" char=".">−.23</td><td align="char" char=".">−.39</td><td align="char" char=".">−.19</td><td align="char" char=".">.08</td><td align="char" char=".">−.42</td></tr><tr><td align="left">I1 + 6</td><td align="char" char=".">−.11</td><td align="char" char=".">−.08</td><td align="char" char=".">.12</td><td align="char" char=".">−.03</td><td align="char" char=".">−.23</td><td align="char" char=".">−.36</td><td align="char" char=".">−.34</td><td align="char" char=".">−.15</td><td align="char" char=".">−.40</td><td align="char" char=".">−.07</td><td align="char" char=".">−.13</td><td align="char" char=".">−.05</td><td align="char" char=".">−.16</td><td align="char" char=".">−.07</td><td align="char" char=".">−.43</td><td align="char" char=".">−.34</td></tr><tr><td align="left">I2 + 6</td><td align="char" char=".">.17</td><td align="char" char=".">.14</td><td align="char" char=".">.06</td><td align="char" char=".">.28</td><td align="char" char=".">−.01</td><td align="char" char=".">.26</td><td align="char" char=".">.18</td><td align="char" char=".">.28</td><td align="char" char=".">.36</td><td align="char" char=".">−.11</td><td align="char" char=".">.25</td><td align="char" char=".">−.01</td><td align="char" char=".">.30</td><td align="char" char=".">.30</td><td align="char" char=".">.32</td><td align="char" char=".">.41</td></tr><tr><td align="left">I3 + 6</td><td align="char" char=".">.26</td><td align="char" char=".">.00</td><td align="char" char=".">.19</td><td align="char" char=".">.00</td><td align="char" char=".">−.19</td><td align="char" char=".">−.36</td><td align="char" char=".">−.34</td><td align="char" char=".">−.15</td><td align="char" char=".">.13</td><td align="char" char=".">.13</td><td align="char" char=".">.18</td><td align="char" char=".">.33</td><td align="char" char=".">−.23</td><td align="char" char=".">.26</td><td align="char" char=".">0.29</td><td align="char" char=".">0.21</td></tr></table> </ephtml> </p> <p>2 p < .05, 2‐tailed.</p> <p></p> <p> <ephtml> <table><tr><th align="left" /><th align="center" /><th align="center">Misconception theme</th></tr><tr><th align="center">Diffusion</th><th align="center">Drift</th><th align="center">Excitation</th></tr><tr><th align="center">1</th><th align="center">2</th><th align="center">3</th><th align="center">4</th><th align="center">5</th><th align="center">1</th><th align="center">2</th><th align="center">3</th><th align="center">1</th><th align="center">2</th><th align="center">3</th><th align="center">4</th><th align="center">6</th></tr><tr><td align="left">Information type theme</td><td align="left" /></tr><tr><td align="left">1</td><td align="char" char=".">0.0146</td><td align="char" char=".">0.0282</td><td align="char" char=".">0.4638</td><td align="char" char=".">0.1587</td><td align="char" char=".">0.1587</td><td align="char" char=".">0.0001</td><td align="char" char=".">0.0005</td><td align="char" char=".">0.0509</td><td align="char" char=".">0.0001</td><td align="char" char=".">0.5000</td><td align="char" char=".">0.0730</td><td align="char" char=".">0.1817</td><td align="char" char=".">0.0282</td></tr><tr><td align="left">2</td><td align="char" char=".">0.0509</td><td align="char" char=".">0.2623</td><td align="char" char=".">0.4638</td><td align="char" char=".">0.0042</td><td align="char" char=".">0.2336</td><td align="char" char=".">0.0042</td><td align="char" char=".">0.0282</td><td align="char" char=".">0.0183</td><td align="char" char=".">0.0003</td><td align="char" char=".">0.3248</td><td align="char" char=".">0.0091</td><td align="char" char=".">0.1587</td><td align="char" char=".">0.0042</td></tr><tr><td align="left">3</td><td align="char" char=".">0.0146</td><td align="char" char=".">0.0864</td><td align="char" char=".">0.0509</td><td align="char" char=".">0.4638</td><td align="char" char=".">0.1587</td><td align="char" char=".">0.0001</td><td align="char" char=".">0.0421</td><td align="char" char=".">0.2928</td><td align="char" char=".">0.1187</td><td align="char" char=".">0.0864</td><td align="char" char=".">0.3248</td><td align="char" char=".">0.0004</td><td align="char" char=".">0.0730</td></tr><tr><td align="left">4</td><td align="char" char=".">0.1187</td><td align="char" char=".">0.2067</td><td align="char" char=".">0.1377</td><td align="char" char=".">0.4279</td><td align="char" char=".">0.0612</td><td align="char" char=".">0.2336</td><td align="char" char=".">0.2067</td><td align="char" char=".">0.0730</td><td align="char" char=".">0.3581</td><td align="char" char=".">0.2336</td><td align="char" char=".">0.4638</td><td align="char" char=".">0.0346</td><td align="char" char=".">0.0282</td></tr><tr><td align="left">5</td><td align="char" char=".">0.0055</td><td align="char" char=".">0.0346</td><td align="char" char=".">0.0183</td><td align="char" char=".">0.1377</td><td align="char" char=".">0.1187</td><td align="char" char=".">0.1587</td><td align="char" char=".">0.0115</td><td align="char" char=".">0.1377</td><td align="char" char=".">0.2623</td><td align="char" char=".">0.4279</td><td align="char" char=".">0.0509</td><td align="char" char=".">0.0864</td><td align="char" char=".">0.3581</td></tr><tr><td align="left">6</td><td align="char" char=".">0.2623</td><td align="char" char=".">0.0864</td><td align="char" char=".">0.0612</td><td align="char" char=".">0.1187</td><td align="char" char=".">0.0509</td><td align="char" char=".">0.4279</td><td align="char" char=".">0.2928</td><td align="char" char=".">0.0421</td><td align="char" char=".">0.3248</td><td align="char" char=".">0.0032</td><td align="char" char=".">0.3581</td><td align="char" char=".">0.0612</td><td align="char" char=".">0.2336</td></tr><tr><td align="left">7</td><td align="char" char=".">0.0183</td><td align="char" char=".">0.1817</td><td align="char" char=".">0.1587</td><td align="char" char=".">0.0509</td><td align="char" char=".">0.4638</td><td align="char" char=".">0.2336</td><td align="char" char=".">0.0071</td><td align="char" char=".">0.1817</td><td align="char" char=".">0.0042</td><td align="char" char=".">0.1187</td><td align="char" char=".">0.0282</td><td align="char" char=".">0.0183</td><td align="char" char=".">0.0002</td></tr><tr><td align="left">Composite</td></tr><tr><td align="left">1 + 6</td><td align="char" char=".">0.1587</td><td align="char" char=".">0.2336</td><td align="char" char=".">0.1377</td><td align="char" char=".">0.3926</td><td align="char" char=".">0.0183</td><td align="char" char=".">0.0005</td><td align="char" char=".">0.0010</td><td align="char" char=".">0.0864</td><td align="char" char=".">0.0001</td><td align="char" char=".">0.2623</td><td align="char" char=".">0.1187</td><td align="char" char=".">0.3248</td><td align="char" char=".">0.0730</td></tr><tr><td align="left">2 + 6</td><td align="char" char=".">0.0612</td><td align="char" char=".">0.1016</td><td align="char" char=".">0.2928</td><td align="char" char=".">0.0055</td><td align="char" char=".">0.4638</td><td align="char" char=".">0.0091</td><td align="char" char=".">0.0509</td><td align="char" char=".">0.0055</td><td align="char" char=".">0.0005</td><td align="char" char=".">0.1587</td><td align="char" char=".">0.0115</td><td align="char" char=".">0.4638</td><td align="char" char=".">0.0032</td></tr><tr><td align="left">3 + 6</td><td align="char" char=".">0.0091</td><td align="char" char=".">0.5000</td><td align="char" char=".">0.0421</td><td align="char" char=".">0.5000</td><td align="char" char=".">0.0421</td><td align="char" char=".">0.0005</td><td align="char" char=".">0.0010</td><td align="char" char=".">0.0864</td><td align="char" char=".">0.1187</td><td align="char" char=".">0.1187</td><td align="char" char=".">0.0509</td><td align="char" char=".">0.0014</td><td align="char" char=".">0.0183</td></tr></table> </ephtml> </p> <p>3 Note. Items in bold are significant.</p> <p>As shown in Table [NaN] , when the participants relied on the knowledge they acquired when they watched the animated simulations along with their prior knowledge, they were more likely to have a misconception if they were utilizing incorrect prior knowledge or a combination of incorrect and correct prior knowledge than if they were utilizing just correct prior knowledge. When the animated simulation did not necessarily dispel the higher level misconception themes observed in participant responses (particularly themes M1, M3, M4, M5, and M6), the use of prior knowledge in combination with knowledge the participants acquired from watching the animated simulations was related to the presence of misconceptions. With theme M2, as shown with drift, the relationship was moderately negative both when participants used correct prior knowledge about that phenomenon and when they used the combination of correct and incorrect prior knowledge for the phenomenon.</p> <hd id="AN0122635334-28">Discussion</hd> <p>We sought to identify students’ misconceptions about three semiconductor phenomena and to determine how students’ use of knowledge is related to the frequency of these misconceptions. We will first discuss the misconceptions and then discuss the relationships between the misconceptions and knowledge use.</p> <hd id="AN0122635334-29">Misconception Identification</hd> <p>Numerous codes emerged from the data representing misconceptions about semiconductors. Some misconceptions of the higher level misconception themes for diffusion and drift have already been identified, but many of the misconception themes (M2, M3, M4, M5, and M6) for these three phenomena had not been reported on in the literature. The misconception themes ranged from misunderstandings about electricity and predictability to those about bonding and energy. As for misconceptions that have already been reported in previous research regarding diffusion in semiconductors (Wettergren et al., [<reflink idref="bib47" id="ref88">47</reflink>] ), the participants in our study also described the movement of electrons as having a certain pattern (theme M2), that electrons move from areas of high concentration of electrons to areas of low concentration of electrons. As found by Chen et al. ([<reflink idref="bib6" id="ref89">6</reflink>] ), participants were confused about the mechanisms for both drift and diffusion as evidenced by their misconceptions about drift and diffusion generally. A study by Fayyaz et al. ([<reflink idref="bib17" id="ref90">17</reflink>] ) reported a misconception that highlighted confusion between conventional current and drift and diffusion current. Even though this study did not consider specific misconceptions related to drift and diffusion current, a misconception was identified about current generally for diffusion and drift (theme M1). In addition to what has already been reported in the literature, we identified misconceptions that electron movement is predictable and not random (M2 and M3), that electrons move even when they are bonded (M4), that other forces, such as gravity, cause electron movement (M5), and that electrons can move between the conduction and valence bands without energy transfer (M6).</p> <p>Some misconceptions captured in the participant responses are associated with other, similar concepts. These include misconceptions related to conservation of energy (M6; e.g., Soloman, [<reflink idref="bib40" id="ref91">40</reflink>] ), electricity and magnetism (M1; e.g., Maloney, [<reflink idref="bib26" id="ref92">26</reflink>] ), quantum mechanics (M2; e.g., Styer, [<reflink idref="bib44" id="ref93">44</reflink>] ), and chemical bonding (theme M4; e.g., Nicoll, [<reflink idref="bib31" id="ref94">31</reflink>] ). In sum, the evidence from our study offers additional confirmation for previously identified misconceptions about semiconductors as well as adding several new ones.</p> <hd id="AN0122635334-30">Misconceptions and Knowledge Use</hd> <p>The relationships observed between the participants’ misconceptions and use of information suggest two findings.</p> <p>First, prior knowledge can lead to the formation of misconceptions. Misconceptions were found to be related to the combination of incorrect prior knowledge and use of knowledge acquired from watching the animated simulations when the simulation was not designed to dispel misconceptions about semiconductors previously reported in the literature.</p> <p>Students develop misconceptions because of how they construct knowledge using their sensory perceptions of the world and through instruction (Nicoll, [<reflink idref="bib31" id="ref95">31</reflink>] ; Resnick et al., [<reflink idref="bib34" id="ref96">34</reflink>] ; Smith et al., 1993). Most undergraduates hold an Aristotelian model of force, a model that they likely developed as small children through interacting with and watching things move about the world (Clement, [<reflink idref="bib9" id="ref97">9</reflink>] ; Clement, [<reflink idref="bib10" id="ref98">10</reflink>] ; Steinberg et al., [<reflink idref="bib41" id="ref99">41</reflink>] ). Our sensory perceptions of the world influence how we construct knowledge and understand phenomena. When participants did not utilize any prior knowledge, their explanations for why the phenomena they watched in the animated simulations exhibited certain behaviors were guided by what they could perceive. These perceptions may explain why participants formed certain misconceptions but not others. However, because themes M1 and I5 were only weakly associated for diffusion, the simulations likely only partly contributed to the students’ misconceptions.</p> <p>Numerous moderate relationships were observed between the combination of prior knowledge (correct, incorrect, and both) with knowledge acquired from the animated simulations and misconceptions. Therefore, even though the simulations could lead to the formation of misconceptions, it is more likely that the misconceptions were due to incorrect prior knowledge or from the participants’ poor attempts to reconcile what they knew (correct or incorrect prior knowledge) with newly acquired knowledge from watching the animated simulations. For themes M1, M4, and M6 (misunderstandings of electricity and magnetism, bonding, and energy, respectively), participants appeared to apply their prior knowledge about electrochemistry and bonding to interpeting the simulations. Resnick et al. ([<reflink idref="bib34" id="ref100">34</reflink>] ) showed that when students attempted to apply their prior knowledge to new concepts, they made errors because they incorrectly combined their prior knowledge with the newly acquired knowledge. For example, students utilized the already‐learned “whole number” rule when attempting to order new types of numbers that they were given. When comparing the decimal numbers 0.25 to 0.5 (new type of number), students would state that 0.25 was greater because 25 is larger than 5 (whole number rule). It is possible that, similar to what Resnick et al. ([<reflink idref="bib34" id="ref101">34</reflink>] ) and Nicoll ([<reflink idref="bib31" id="ref102">31</reflink>] ) reported (see Introduction section), some of the misconceptions identified in this study occurred because participants attempted to integrate their prior knowledge with what they watched in the simulations.</p> <p>Second, knowledge acquired from watching the animated simulations was not enough to change participants’ understandings to be completely correct. When using the knowledge acquired from watching the animated simulations, participants still exhibited misconceptions when they had both incorrect and correct prior knowledge.</p> <p>The animated simulations depicted the phenomena by correctly showing the random and unpredictable actions of the electrons in the simulations, visually dispelling possible misconceptions present in theme M2 (electron movement is not random and is predictable behavior), but not themes M1, M3, M4, M5, or M6. Despite the way that the animated simulations depicted the phenomena, theme M2 was especially prevalent in participants’ responses in the diffusion and drift modules and moderately prevalent in the excitation module. As Sinatra et al. ([<reflink idref="bib38" id="ref103">38</reflink>] ) described, students’ incorrect prior knowledge reinforced their understanding of the phenomena. The participants likely took their newly acquired knowledge from watching the animated simulations and used it to support their existing misconceptions. Many of the misconceptions may have been reinforced because the participants were not made aware that they had the misconception (Evans et al., [<reflink idref="bib16" id="ref104">16</reflink>] ) or were not taught what common misconceptions are when learning about these semiconductor phenomena. Without deliberately making students aware of these misconceptions, educators’ continued use of educational tools may do little to aid students in overcoming misconceptions, especially because it is hard for students to overcome misconceptions (Dole & Sinatra, [<reflink idref="bib14" id="ref105">14</reflink>] ).</p> <hd id="AN0122635334-31">Implications</hd> <p>The prevalence of misconceptions in participants’ responses demonstrates a problem in students understanding of semiconductors and other fundamental science concepts. Some of the misconceptions indicate a lack of fundamental knowledge that participants should have learned in chemistry, physics, and materials science courses. For example, the higher level misconception in theme M4 is an inaccurate understanding of the difference between bonded atoms and singular atoms in space. The participants in our study should have encountered this fundamental concept in high school chemistry, yet they still had misconceptions that impede their understanding of semiconductors.</p> <p>The presence of misconceptions in participants’ responses can have implications for how they understand semiconductors; this misunderstanding could lead to poor engineering designs for semiconductors. For example, theme M1 (electron movement is associated with a charge or potential difference) was prevalent in participants’ responses for each phenomenon. Good designs for semiconductors are contingent upon accurate calculations for diffusion and drift current. If students believe that electrons move only because of an applied voltage, then they may not include diffusion current in their calculations. Overall, the misconceptions students held about diffusion, drift, and excitation indicate a lack of understanding of aspects of current, voltage, and ultimately, power generation for semiconductors.</p> <p>Certain misconception themes we observed for all phenomena are not especially semiconductor‐specific. For example, theme M4 refers to misunderstandings related to bonding, misunderstandings that are being applied to concepts for electrical circuits; bonding is also fundamental subject matter covered in chemistry and physics. Students could easily have this same misconception for other phenomena in physics. The misconceptions observed in this study can be used to determine what misconceptions students have in other fields and inform how students understand concepts in those fields.</p> <p>If engineering educators do not properly inform students about misconceptions and intervene, the types of simulations we use could inadvertently reinforce existing misconceptions. Chi et al. ([<reflink idref="bib8" id="ref106">8</reflink>] ) have shown that the use of animated simulations for phenomena that are dynamic and unobservable can aid students in overcoming misconceptions. Their simulations were constructed by learning scientists to conduct research on conceptual change. In our study, although participants viewed correct, expert‐approved simulations of the phenomena, they still exhibited misconceptions in 80% of their responses. The high frequency of misconceptions found in participants’ responses in this study, along with Chi et al.’s ([<reflink idref="bib8" id="ref107">8</reflink>] ) results, may indicate a need to develop simulations in engineering education that are guided by learning scientists and educational technologists who are more equipped to incorporate features that will promote student's understanding of the phenomena.</p> <p>Our findings may assist educators when they teach these phenomena. Educators could tailor course material to dispel the misconception themes that were the most prevalent among our participants. Engineering educators are pressed for time; they rarely cover all of the material that is in their syllabus (Sheppard, Macatangy, Colby, & Sullivan, [<reflink idref="bib36" id="ref108">36</reflink>] ). However, saving time by focusing on prevalent misconceptions does not help those students who have other misconceptions. Instructors need to know what misconceptions exist for what is being covered and for the course material that students should already have learned. Students should be made aware either that they have a particular misconception or that certain misconceptions are associated with the concepts they are learning. For example, when educators teach semiconductor courses, they should address students’ misconceptions about covalent bonds. Finally, educators need to better understand how their educational tools may hinder students from changing their misconceptions. As was the case in our study, certain misconception themes were not dispelled in the simulations. When instructors utilize tools such as simulations or other representations about scientific phenomena, it would be best done if misconceptions were considered in the design of these tools</p> <hd id="AN0122635334-32">Conclusions</hd> <p>This study demonstrated that undergraduate engineering students have misconceptions about semiconductor phenomena and that the simulations we use to teach students about these phenomena may be reinforcing or aiding in the development of these misconceptions. Misconception themes were associated with poor understanding of physics and chemistry concepts that the participants should have acquired prior to their undergraduate coursework. Students showed fewer misconceptions when they applied correct prior knowledge. Those students who relied on the knowledge acquired from watching the animated simulations while also using incorrect prior knowledge had misconceptions. It is likely that the animated simulations were not useful in the prevention of misconceptions, but they did allow us to see what misconceptions participants had about semiconductors. Overall, these findings demonstrate the need for further research that helps identify misconception students have or develop as they study semiconductors, as well as provide insight to educators about how to develop simulations for students to encourage correct concept formation.</p> <hd id="AN0122635334-33">Acknowledgments</hd> <p>We would like to thank Dr. Stuart Bowden and Dr. Christiana Honsberg, for use of the animated simulations originally developed for pveducation.org as well as for their expertise and guidance on the semiconductor concepts.</p> <p>This research is based upon work primarily supported by the National Science Foundation (NSF) and the Department of Energy (DOE) under NSF CA No. EEC‐1041895. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect those of NSF and DOE.</p> <hd id="AN0122635334-34">Appendix A: AInstrument Questions</hd> <p>Simulation 1 (Diffusion)</p> <p>1. Describe the movement of the electrons in the solar cell. Use as much detail as possible. 1. Describe the movement of the electrons in the solar cell. Use as much detail as possible.</p> <ulist> <item>2. Based on your knowledge of physics and electrons, what determines how and where the electrons move in the solar cell? Use as much detail as possible. 2. Based on your knowledge of physics and electrons, what determines how and where the electrons move in the solar cell? Use as much detail as possible.</item> <item>3. Imagine electrons in a similar solar cell under the same scenario moving again. How similar do you think the movement of the electrons would be to what you observed in the video? Please choose one answer, your best estimate. (A. very similar, B. somewhat similar, C. somewhat dissimilar, D. very dissimilar) 3. Imagine electrons in a similar solar cell under the same scenario moving again. How similar do you think the movement of the electrons would be to what you observed in the video? Please choose one answer, your best estimate. (A. very similar, B. somewhat similar, C. somewhat dissimilar, D. very dissimilar)</item> <item>4. What do you know about electron movement inside a material that made you answer the previous question that way? Use as much detail as possible. 4. What do you know about electron movement inside a material that made you answer the previous question that way? Use as much detail as possible.</item> <item>5. Is the movement of the electrons in the material random? (A. definitely yes, B. probably yes, C. probably not, D. definitely not) 5. Is the movement of the electrons in the material random? (A. definitely yes, B. probably yes, C. probably not, D. definitely not)</item> <item>6. What do you know about electron movement inside a material that made you answer the previous question that way? Use as much detail as possible. 6. What do you know about electron movement inside a material that made you answer the previous question that way? Use as much detail as possible.</item> <item>7. Is the movement of the electrons in the material rule‐based? (A. definitely yes, B. probably yes, C. probably not, D. definitely not) 7. Is the movement of the electrons in the material rule‐based? (A. definitely yes, B. probably yes, C. probably not, D. definitely not)</item> <item>8. What do you know about electron movement inside a material that made you answer the previous question that way? Use as much detail as possible. 8. What do you know about electron movement inside a material that made you answer the previous question that way? Use as much detail as possible.</item> </ulist> <p>Simulation 2 (Drift)</p> <ulist> <item>9. Describe the movement of the electrons in the solar cell. Use as much detail as possible. 9. Describe the movement of the electrons in the solar cell. Use as much detail as possible.</item> <item>10. Based on your knowledge of physics and electrons, what determines how and where the electrons move in the solar cell when the electric field is off? Use as much detail as possible 10. Based on your knowledge of physics and electrons, what determines how and where the electrons move in the solar cell when the electric field is off? Use as much detail as possible</item> <item>11. Based on your knowledge of physics and electrons, what determines how and where the electrons move in the solar cell when the electric field is on? Use as much detail as possible. Imagine electrons in a similar solar cell under the same scenario moving again. How similar do you think the movement of the electrons would be to what you observed in the video when the electric field is off? Please choose one answer, your best estimate. (A. very similar, B. somewhat similar, C. somewhat dissimilar, D. very dissimilar) 11. Based on your knowledge of physics and electrons, what determines how and where the electrons move in the solar cell when the electric field is on? Use as much detail as possible. Imagine electrons in a similar solar cell under the same scenario moving again. How similar do you think the movement of the electrons would be to what you observed in the video when the electric field is off? Please choose one answer, your best estimate. (A. very similar, B. somewhat similar, C. somewhat dissimilar, D. very dissimilar)</item> <item>12. What do you know about electron movement inside a material that made you answer the previous question that way? Use as much detail as possible. 12. What do you know about electron movement inside a material that made you answer the previous question that way? Use as much detail as possible.</item> <item>13. Imagine electrons, in a similar solar cell, under the same scenario, moving again. How similar do you think the movement of the electrons would be to what you observed in the video when the electric field is on? Please choose one answer, your best estimate. (A. very similar, B. somewhat similar, C. somewhat dissimilar, D. very dissimilar) 13. Imagine electrons, in a similar solar cell, under the same scenario, moving again. How similar do you think the movement of the electrons would be to what you observed in the video when the electric field is on? Please choose one answer, your best estimate. (A. very similar, B. somewhat similar, C. somewhat dissimilar, D. very dissimilar)</item> <item>14. What do you know about electron movement inside a material that made you answer the previous question that way? Use as much detail as possible. 14. What do you know about electron movement inside a material that made you answer the previous question that way? Use as much detail as possible.</item> <item>15. Is the movement of the electrons in the material random? A. definitely yes, B. probably yes, C. probably not, D. definitely not) 15. Is the movement of the electrons in the material random? A. definitely yes, B. probably yes, C. probably not, D. definitely not)</item> <item>16. What do you know about electron movement inside a material that made you answer the previous question that way? Use as much detail as possible. 16. What do you know about electron movement inside a material that made you answer the previous question that way? Use as much detail as possible.</item> <item>17. Is the movement of the electrons in the material rule‐based? (A. definitely yes, B. probably yes, C. probably not, D. definitely not) 17. Is the movement of the electrons in the material rule‐based? (A. definitely yes, B. probably yes, C. probably not, D. definitely not)</item> <item>18. What do you know about electron movement inside a material that made you answer the previous question that way? Use as much detail as possible. 18. What do you know about electron movement inside a material that made you answer the previous question that way? Use as much detail as possible.</item> </ulist> <p>Simulation 3 (Excitation)</p> <ulist> <item>19. Describe the movement of the electrons in the solar cell. Use as much detail as possible. 19. Describe the movement of the electrons in the solar cell. Use as much detail as possible.</item> <item>20. Based on your knowledge of physics, what determines how and where the electron moves in the solar cell during each photon event? Please use as much detail as possible. 20. Based on your knowledge of physics, what determines how and where the electron moves in the solar cell during each photon event? Please use as much detail as possible.</item> <item>21. Based on your knowledge of physics, why does the electron move to the conduction band? Why does it move within the conduction band? Use as much detail as possible. 21. Based on your knowledge of physics, why does the electron move to the conduction band? Why does it move within the conduction band? Use as much detail as possible.</item> <item>22. Imagine an electron, in a similar solar cell, under the same scenario, moving again. How similar do you think the movement of the electron would be to what you observed in the video during each photon event? Please choose one answer, your best estimate. (A. very similar, B. somewhat similar, C. somewhat dissimilar, D. very dissimilar) 22. Imagine an electron, in a similar solar cell, under the same scenario, moving again. How similar do you think the movement of the electron would be to what you observed in the video during each photon event? Please choose one answer, your best estimate. (A. very similar, B. somewhat similar, C. somewhat dissimilar, D. very dissimilar)</item> <item>23. What do you know about electron movement inside a material that made you answer the previous question that way? Use as much detail as possible. 23. What do you know about electron movement inside a material that made you answer the previous question that way? Use as much detail as possible.</item> <item>24. Based on your knowledge of physics, does an electron move in a solar cell if there are no photons? (A. definitely yes, B. probably yes, C. probably not, D. definitely not) 24. Based on your knowledge of physics, does an electron move in a solar cell if there are no photons? (A. definitely yes, B. probably yes, C. probably not, D. definitely not)</item> <item>25. What do you know about electron movement that made you answer the previous question that way? Use as much detail as possible. 25. What do you know about electron movement that made you answer the previous question that way? Use as much detail as possible.</item> <item>26. Is the movement of the electron in the material during the photon event rule‐based? (A. definitely yes, B. probably yes, C. probably not, D. definitely not) 26. Is the movement of the electron in the material during the photon event rule‐based? (A. definitely yes, B. probably yes, C. probably not, D. definitely not)</item> <item>27. What do you know about electron movement that made you answer the previous question that way? Use as much detail as possible. 27. What do you know about electron movement that made you answer the previous question that way? Use as much detail as possible.</item> </ulist> <hd id="AN0122635334-35">Appendix B: Correlation Probabilities</hd> <p>Table B1 Kendall's tau‐b Correlation Probabilities for Misconception and Information Type</p> <hd id="AN0122635334-36">Appendix Appendix C Correlation Composite Probabilities</hd> <p>Kendall's tau‐b Correlation Probabilities for Misconception and Information Type</p> <p> <ephtml> <table><tr><th align="center" /><th align="center">Diffusion</th><th align="center">Drift</th><th align="center">Excitation</th></tr><tr><td align="left">Information type theme</td></tr><tr><td align="left"> 1</td><td align="char" char=".">0.0071</td><td align="char" char=".">0.0001</td><td align="char" char=".">0.0003</td></tr><tr><td align="left"> 2</td><td align="char" char=".">0.0228</td><td align="char" char=".">0.0004</td><td align="char" char=".">0.0003</td></tr><tr><td align="left"> 3</td><td align="char" char=".">0.0421</td><td align="char" char=".">0.0014</td><td align="char" char=".">0.0183</td></tr><tr><td align="left"> 4</td><td align="char" char=".">0.3248</td><td align="char" char=".">0.0509</td><td align="char" char=".">0.1187</td></tr><tr><td align="left"> 5</td><td align="char" char=".">0.2928</td><td align="char" char=".">0.3926</td><td align="char" char=".">0.1377</td></tr><tr><td align="left"> 6</td><td align="char" char=".">0.0228</td><td align="char" char=".">0.2928</td><td align="char" char=".">0.2928</td></tr><tr><td align="left"> 7</td><td align="char" char=".">0.0421</td><td align="char" char=".">0.2336</td><td align="char" char=".">0.0001</td></tr><tr><td align="left">Composite</td></tr><tr><td align="left"> 1 + 6</td><td align="char" char=".">0.2623</td><td align="char" char=".">0.0001</td><td align="char" char=".">0.0010</td></tr><tr><td align="left"> 2 + 6</td><td align="char" char=".">0.0032</td><td align="char" char=".">0.0018</td><td align="char" char=".">0.0001</td></tr><tr><td align="left"> 3 + 6</td><td align="char" char=".">0.0091</td><td align="char" char=".">0.0042</td><td align="char" char=".">0.0282</td></tr></table> </ephtml> </p> <p>4 Note. 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Brem; Jonathan Hilpert; Jenefer Husman and Eva Pettinato</p> <p></p> <p>Katherine G. Nelson is an instructor and part‐time researcher in the Experiential Engineering Education Department in the College of Engineering at Rowan University, Glassboro, NJ, 08028,.</p> <p>Ann. F. McKenna is a professor of engineering and director of the Polytechnic School in the Ira A. Fulton Schools of Engineering at Arizona State University, Mesa, AZ, 85212,.</p> <p>Sarah K. Brem is a retired professor from the T. Denny Sanford School of Social and Family Dynamics at Arizona State University and currently resides in Eugene, Oregon.</p> <p>Jonathan C. Hilpert is an associate professor of educational psychology in the Department of Curriculum, Reading, and Foundations in the College of Education at Georgia Southern University, Statesboro, GA, 30458,.</p> <p>Jenefer Husman is an associate editor for JEE and an associate professor in the Department of Educational Studies in the College of Education at the University of Oregon, Eugene, OR, 97403,.</p> <p>Eva Pettinato is an aerospace engineer in the Guidance, Navigation and Controls Department at Orbital ATK in Chandler, AZ, 85248,.</p> </aug> <nolink nlid="nl1" bibid="bib82" firstref="ref86"></nolink>
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  Label: Title
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  Data: Students' Misconceptions about Semiconductors and Use of Knowledge in Simulations
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  Data: English
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Nelson%2C+Katherine+G%2E%22">Nelson, Katherine G.</searchLink><br /><searchLink fieldCode="AR" term="%22McKenna%2C+Ann+F%2E%22">McKenna, Ann F.</searchLink><br /><searchLink fieldCode="AR" term="%22Brem%2C+Sarah+K%2E%22">Brem, Sarah K.</searchLink><br /><searchLink fieldCode="AR" term="%22Hilpert%2C+Jonathan%22">Hilpert, Jonathan</searchLink><br /><searchLink fieldCode="AR" term="%22Husman%2C+Jenefer%22">Husman, Jenefer</searchLink><br /><searchLink fieldCode="AR" term="%22Pettinato%2C+Eva%22">Pettinato, Eva</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Engineering+Education%22"><i>Journal of Engineering Education</i></searchLink>. Apr 2017 106(2):218-244.
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  Data: Wiley Periodicals, Inc. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA
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  Data: Y
– Name: Pages
  Label: Page Count
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  Data: 27
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2017
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  Data: Journal Articles<br />Reports - Research
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  Label: Education Level
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  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+Education%22">Engineering Education</searchLink><br /><searchLink fieldCode="DE" term="%22Misconceptions%22">Misconceptions</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+Level%22">Knowledge Level</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+Equipment%22">Electronic Equipment</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation%22">Simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Prior+Learning%22">Prior Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Scientific+Concepts%22">Scientific Concepts</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1002/jee.20163
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1069-4730
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: Little research exists on students' misconceptions about semiconductors, why they form, and what role educational resources like simulations play in misconception formation. Research on misconceptions can help enhance student learning about semiconductors. Purpose (Hypothesis): This project sought to identify students' misconceptions about three semiconductor phenomena -- diffusion, drift, and excitation -- and to determine if prior knowledge, knowledge acquired from watching animated simulations, or both were related to students' misconceptions. We hypothesized that students would hold misconceptions about those phenomena and that students' prior knowledge and knowledge acquired from watching animated simulations would be associated with their misconceptions. Design/Method: Forty-one engineering students completed an instrument that asked questions about three semiconductor phenomena after the students had observed the animated simulations. Responses were analyzed and coded using two frameworks: misconception and knowledge use. Results: Misconceptions were prevalent for all three phenomena. Misconceptions were associated with use of incorrect prior knowledge, a combination of correct or incorrect prior knowledge, and the knowledge acquired from watching the animated simulations alone or in combination with correct and incorrect prior knowledge. Misconceptions indicated a lack of understanding of chemistry and physics concepts. Conclusions: Findings indicate that students hold many misconceptions about semiconductor phenomena. These misconceptions were common among our participants. The knowledge acquired from the animated simulations alone or in combination with prior knowledge could reinforce or contribute to misconception formation. Our findings can guide instructors to use or create better simulations to aid student learning.
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  Data: 2020
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  Data: EJ1254474
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        Value: 10.1002/jee.20163
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      – Text: English
    PhysicalDescription:
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        PageCount: 27
        StartPage: 218
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      – SubjectFull: College Students
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      – SubjectFull: Misconceptions
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      – TitleFull: Students' Misconceptions about Semiconductors and Use of Knowledge in Simulations
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